Artificial Intelligence for Diabetes Management and Decision Support: Literature Review
By Iván Contreras, Josep Vehí. Journal of Medical Internet Research, 2018
465 citations recorded by the source corpus.
Background Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.
Subfields: Artificial Intelligence, Health Informatics, Health Information Management
View Paper: https://www.semanticscholar.org/paper/0adfae854ebf47d6f5a5c0f9c1033279793e62e0
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Citations in the corpus
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “One stream of reviews provided an overview of the current status of AI technology in specific clinical domains, such as breast cancer diagnosis [14], melanoma diagnosis [15], pulmonary tuberculosis diagnosis [16], stroke diagnosis and prediction [17], and diabetes management [18].”
- Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes (2019, background): “…reviews on the applications of artificial intelligence in the context of critical diabetes management issues such as BG prediction and strategies for BG control, adverse glycemic events detection, bolus calculators and advisory system, patient personalization (tailored features), and others [8].”